pip install litellm

from litellm import completion
import os

os.environ["OPENAI_API_KEY"] = "your-openai-key"
os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-key"

# OpenAI
response = completion(model="openai/gpt-4o", messages=[{"role": "user", "content": "Hello!"}])

# Anthropic  
response = completion(model="anthropic/claude-sonnet-4-20250514", messages=[{"role": "user", "content": "Hello!"}])

pip install 'litellm[proxy]'
litellm --model gpt-4o

import openai

client = openai.OpenAI(api_key="anything", base_url="http://0.0.0.0:4000")
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}]
)

from litellm.a2a_protocol import A2AClient
from a2a.types import SendMessageRequest, MessageSendParams
from uuid import uuid4

client = A2AClient(base_url="http://localhost:10001")

request = SendMessageRequest(
    id=str(uuid4()),
    params=MessageSendParams(
        message={
            "role": "user",
            "parts": [{"kind": "text", "text": "Hello!"}],
            "messageId": uuid4().hex,
        }
    )
)
response = await client.send_message(request)

from a2a.client import A2ACardResolver, A2AClient
from a2a.types import MessageSendParams, SendMessageRequest
from uuid import uuid4
import httpx

base_url = "http://localhost:4000/a2a/my-agent"  # LiteLLM 代理 + 智能体名称
headers = {"Authorization": "Bearer sk-1234"}    # LiteLLM 虚拟密钥

async with httpx.AsyncClient(headers=headers) as httpx_client:
    resolver = A2ACardResolver(httpx_client=httpx_client, base_url=base_url)
    agent_card = await resolver.get_agent_card()
    client = A2AClient(httpx_client=httpx_client, agent_card=agent_card)

    request = SendMessageRequest(
        id=str(uuid4()),
        params=MessageSendParams(
            message={
                "role": "user",
                "parts": [{"kind": "text", "text": "Hello!"}],
                "messageId": uuid4().hex,
            }
        )
    )
    response = await client.send_message(request)

from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
from litellm import experimental_mcp_client
import litellm

server_params = StdioServerParameters(command="python", args=["mcp_server.py"])

async with stdio_client(server_params) as (read, write):
    async with ClientSession(read, write) as session:
        await session.initialize()

        # 以 OpenAI 格式加载 MCP 工具
        tools = await experimental_mcp_client.load_mcp_tools(session=session, format="openai")

        # 与任何 LiteLLM 模型一起使用
        response = await litellm.acompletion(
            model="gpt-4o",
            messages=[{"role": "user", "content": "What's 3 + 5?"}],
            tools=tools
        )

curl -X POST 'http://0.0.0.0:4000/v1/chat/completions' \
  -H 'Authorization: Bearer sk-1234' \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "gpt-4o",
    "messages": [{"role": "user", "content": "Summarize the latest open PR"}],
    "tools": [{
      "type": "mcp",
      "server_url": "litellm_proxy/mcp/github",
      "server_label": "github_mcp",
      "require_approval": "never"
    }]
  }'

{
  "mcpServers": {
    "LiteLLM": {
      "url": "http://localhost:4000/mcp/",
      "headers": {
        "x-litellm-api-key": "Bearer sk-1234"
      }
    }
  }
}